Why LLM Moderation Flags Misfire and How a Three-Route System Fixes It

AI content moderation tools generate false positives when a classifier's label is treated as a final policy decision, rather than one input among many. A three-route framework — allow, review, and block — helps by reserving outright rejection for only narrow, high-confidence rule violations while routing ambiguous cases to human reviewers. Context loss compounds the problem: truncated text, multilingual normalization, and schema rounding can strip the nuance needed to judge whether flagged language reflects prohibited intent or legitimate professional discussion. Different tenants may also apply different content rules to the same signal, meaning routing logic must be policy-aware rather than tied to a generic score threshold. Retaining raw category scores, the policy version, the model identifier, and the evaluated text together ensures operators can reconstruct and audit any moderation decision after the fact.